
from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


UpperCAmelCase       =					logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCAmelCase       =					'''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")
        >>> pipe_prior.to("cuda")
        >>> prompt = "red cat, 4k photo"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")
        >>> pipe.to("cuda")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save("cat.png")
        ```
'''






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE=8				):
	lowercase	    =       height // scale_factor**2
	if height % scale_factor**2 != 0:
		new_height += 1
	lowercase	    =       width // scale_factor**2
	if width % scale_factor**2 != 0:
		new_width += 1
	return new_height * scale_factor, new_width * scale_factor







class 			A_  (     __lowerCamelCase  ):




	'''simple docstring'''


	def __init__(			self						,       snake_case						,       snake_case						,       snake_case						,       ):
		super().__init__()

		self.register_modules(
		    unet=snake_case						,       scheduler=snake_case						,       movq=snake_case						,       )
		lowercase	    =       2 ** (len(self.movq.config.block_out_channels			) - 1)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case			):
		if latents is None:
			lowercase	    =       randn_tensor(snake_case						,       generator=snake_case						,       device=snake_case						,       dtype=snake_case			)
		else:
			if latents.shape != shape:
				raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''			)
			lowercase	    =       latents.to(snake_case			)

		lowercase	    =       latents * scheduler.init_noise_sigma
		return latents





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case=0			):
		if is_accelerate_available():
			from accelerate import cpu_offload
		else:
			raise ImportError('Please install accelerate via `pip install accelerate`'			)

		lowercase	    =       torch.device(F'''cuda:{gpu_id}'''			)

		lowercase	    =       [
		    self.unet,
		    self.movq,
		]
		for cpu_offloaded_model in models:
			if cpu_offloaded_model is not None:
				cpu_offload(snake_case						,       snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case=0			):
		if is_accelerate_available() and is_accelerate_version('>='						,       '0.17.0.dev0'			):
			from accelerate import cpu_offload_with_hook
		else:
			raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'			)

		lowercase	    =       torch.device(F'''cuda:{gpu_id}'''			)

		if self.device.type != "cpu":
			self.to('cpu'						,       silence_dtype_warnings=snake_case			)
			torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

		lowercase	    =       None
		for cpu_offloaded_model in [self.unet, self.movq]:
			lowercase    ,   lowercase	    =       cpu_offload_with_hook(snake_case						,       snake_case						,       prev_module_hook=snake_case			)

		# We'll offload the last model manually.
		lowercase	    =       hook





	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def       SCREAMING_SNAKE_CASE__		(			self			):
		if not hasattr(self.unet						,       '_hf_hook'			):
			return self.device
		for module in self.unet.modules():
			if (
			    hasattr(snake_case						,       '_hf_hook'			)
			    and hasattr(module._hf_hook						,       'execution_device'			)
			    and module._hf_hook.execution_device is not None
			):
				return torch.device(module._hf_hook.execution_device			)
		return self.device





	@torch.no_grad()
	@replace_example_docstring(snake_case			)
	def __call__(			self						,       snake_case						,       snake_case						,       snake_case = 512						,       snake_case = 512						,       snake_case = 100						,       snake_case = 4.0						,       snake_case = 1						,       snake_case = None						,       snake_case = None						,       snake_case = "pil"						,       snake_case = True						,       ):
		lowercase	    =       self._execution_device

		lowercase	    =       guidance_scale > 1.0

		if isinstance(snake_case						,       snake_case			):
			lowercase	    =       torch.cat(snake_case						,       dim=0			)
		lowercase	    =       image_embeds.shape[0] * num_images_per_prompt
		if isinstance(snake_case						,       snake_case			):
			lowercase	    =       torch.cat(snake_case						,       dim=0			)

		if do_classifier_free_guidance:
			lowercase	    =       image_embeds.repeat_interleave(snake_case						,       dim=0			)
			lowercase	    =       negative_image_embeds.repeat_interleave(snake_case						,       dim=0			)

		lowercase	    =       torch.cat([negative_image_embeds, image_embeds]						,       dim=0			).to(dtype=self.unet.dtype						,       device=snake_case			)

		self.scheduler.set_timesteps(snake_case						,       device=snake_case			)
		lowercase	    =       self.scheduler.timesteps

		lowercase	    =       self.unet.config.in_channels

		lowercase    ,   lowercase	    =       downscale_height_and_width(snake_case						,       snake_case						,       self.movq_scale_factor			)

		# create initial latent
		lowercase	    =       self.prepare_latents(
		    (batch_size, num_channels_latents, height, width)						,       image_embeds.dtype						,       snake_case						,       snake_case						,       snake_case						,       self.scheduler						,       )

		for i, t in enumerate(self.progress_bar(snake_case			)			):
			# expand the latents if we are doing classifier free guidance
			lowercase	    =       torch.cat([latents] * 2			) if do_classifier_free_guidance else latents

			lowercase	    =       {'image_embeds': image_embeds}
			lowercase	    =       self.unet(
			    sample=snake_case						,       timestep=snake_case						,       encoder_hidden_states=snake_case						,       added_cond_kwargs=snake_case						,       return_dict=snake_case						,       )[0]

			if do_classifier_free_guidance:
				lowercase    ,   lowercase	    =       noise_pred.split(latents.shape[1]						,       dim=1			)
				lowercase    ,   lowercase	    =       noise_pred.chunk(2			)
				lowercase    ,   lowercase	    =       variance_pred.chunk(2			)
				lowercase	    =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
				lowercase	    =       torch.cat([noise_pred, variance_pred_text]						,       dim=1			)

			if not (
			    hasattr(self.scheduler.config						,       'variance_type'			)
			    and self.scheduler.config.variance_type in ["learned", "learned_range"]
			):
				lowercase    ,   lowercase	    =       noise_pred.split(latents.shape[1]						,       dim=1			)

			# compute the previous noisy sample x_t -> x_t-1
			lowercase	    =       self.scheduler.step(
			    snake_case						,       snake_case						,       snake_case						,       generator=snake_case						,       )[0]
		# post-processing
		lowercase	    =       self.movq.decode(snake_case						,       force_not_quantize=snake_case			)['sample']

		if output_type not in ["pt", "np", "pil"]:
			raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''			)

		if output_type in ["np", "pil"]:
			lowercase	    =       image * 0.5 + 0.5
			lowercase	    =       image.clamp(0						,       1			)
			lowercase	    =       image.cpu().permute(0						,       2						,       3						,       1			).float().numpy()

		if output_type == "pil":
			lowercase	    =       self.numpy_to_pil(snake_case			)

		if not return_dict:
			return (image,)

		return ImagePipelineOutput(images=snake_case			)



"""simple docstring"""






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class 			lowercase					( lowercase__		):
			lowercase        =     ['''image_processor''', '''tokenizer''']
			lowercase        =     '''CLIPImageProcessor'''
			lowercase        =     ('''XLMRobertaTokenizer''', '''XLMRobertaTokenizerFast''')






			def __init__(self  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	int=None      ,SCREAMING_SNAKE_CASE_  :	str=None      ,**SCREAMING_SNAKE_CASE_  :	Union[str, Any]			)       ->	int:





				"""simple docstring"""


				lowerCAmelCase												=  None
				if "feature_extractor" in kwargs:
					warnings.warn(
					    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
					    ''' instead.'''      ,SCREAMING_SNAKE_CASE_      ,)
					lowerCAmelCase												=  kwargs.pop('''feature_extractor'''			)

				lowerCAmelCase												=  image_processor if image_processor is not None else feature_extractor
				if image_processor is None:
					raise ValueError('''You need to specify an `image_processor`.'''			)
				if tokenizer is None:
					raise ValueError('''You need to specify a `tokenizer`.'''			)

				super().__init__(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)






			def __call__(self  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	List[str]=None      ,SCREAMING_SNAKE_CASE_  :	Optional[int]=None      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]=None      ,**SCREAMING_SNAKE_CASE_  :	Any			)       ->	List[Any]:





				"""simple docstring"""


				if text is None and images is None:
					raise ValueError('''You have to specify either text or images. Both cannot be none.'''			)

				if text is not None:
					lowerCAmelCase												=  self.tokenizer(SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)

				if images is not None:
					lowerCAmelCase												=  self.image_processor(SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)

				if text is not None and images is not None:
					lowerCAmelCase												=  image_features.pixel_values
					return encoding
				elif text is not None:
					return encoding
				else:
					return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_			)      ,tensor_type=SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	List[str]      ,*SCREAMING_SNAKE_CASE_  :	Union[str, Any]      ,**SCREAMING_SNAKE_CASE_  :	Tuple			)       ->	Optional[Any]:





				"""simple docstring"""


				return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Union[str, Any]      ,*SCREAMING_SNAKE_CASE_  :	int      ,**SCREAMING_SNAKE_CASE_  :	Union[str, Any]			)       ->	Any:





				"""simple docstring"""


				return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)






			@property
			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	Dict:





				"""simple docstring"""


				lowerCAmelCase												=  self.tokenizer.model_input_names
				lowerCAmelCase												=  self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)


